MEASUREMENTS OF STELLAR MAGNETIC FIELDS USING AUTOCORRELATION OF SPECTRA
Bibliographic record
Abstract
We present a novel technique that uses the autocorrelation of the spectrum of a star to measure the line broadening caused by the modulus of its average surface magnetic field. The advantage of the autocorrelation comes from the fact that it can detect very small spectral line broadening effects because it averages over many spectral lines and therefore gives an average with a very high signal-to-noise ratio. We validate the technique with the spectra of known magnetic stars and obtain autocorrelation curves that are in full agreement with published magnetic curves obtained with Zeeman splitting. The autocorrelation also gives less noisy curves so that it can be used to obtain very accurate curves. We degrade the resolution of the spectra of these magnetic stars to lower spectral resolutions where the Zeeman splitting is undetectable. At these resolutions, the autocorrelation still gives good quality curves, thereby showing that it can be used to measure magnetic fields in spectra where the Zeeman splitting is significantly smaller than the width of the spectral line. This would therefore allow observing magnetic fields in very faint Ap stars with low-resolution spectrographs, thereby greatly increasing the number of known magnetic stars. It also demonstrates that the autocorrelation can measure magnetic fields in rapidly rotating stars as well as weak magnetic fields that give a Zeeman splitting smaller than the intrinsic width of the spectral lines. Finally, it shows that the autocorrelation can be used to find unknown magnetic stars in low-resolution spectroscopic surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".